Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: a machine learning approach to Precambrian greenstone belts

Abstract

Mineral exploration workflows are including more quantitative techniques and probabilistic targeting to capture subtle or convoluted relationships to gain insight about geological processes. However, related methodological improvements often focus on efficiency and sensitivity, leaving geologically representative feature engineering underdeveloped. Improving modeling capabilities alone is insufficient, and both geological plausibility and representation of complex processes or features are critical to generate robust predictive models. Geological features involved in Magmatic Ni-Cu-PGE, Volcanogenic Massive Sulfide (VMS) Cu-Zn-Pb-Ag(-Au), and Orogenic Au mineral system prospectivity from two Archean greenstone belts from the southern Superior Province near Timmins and Dryden, Ontario are examined and compared using a variety of statistical techniques. Specifically, this PhD research explores 1) current knowledge and characteristic geological features for both greenstone belts, 2) methods to enhance geological knowledge using whole rock geochemistry, 3) methods to reduce bias and improve repeatability when mapping structural complexity, 4) how data science and geological understanding of mineral systems can be integrated for enhanced feature engineering 5) which factors control, or are most strongly associated with mineralization, and 6) why the greenstone belts near Timmins and Dryden, Ontario have contrasting orogenic Au endowment. Data-related outcomes of this research include multi-disciplinary geoscientific databases (e.g., structural, field observations, geochemistry), new bedrock geology maps for each area, and reprocessed aeromagnetic grids. Methodological outcomes of this research include new geochemical classification diagrams for ultramafic to felsic (including tonalite- trondhjemite -granodiorite and lamprophyre) Archean igneous rocks, Igneous Rock Favorability indices, automatic mapping of structural complexity from bedding measurements and aeromagnetic lineaments, mapping pre-deformation fluid path distances, mapping rheological and chemical contrast, semi-discrete interpolation of characteristic element ratios, as well as mapping mobile element gain/loss. Geological knowledge outcomes include the importance ranking of factors controlling magmatic, volcanogenic, and orogenic prospectivity from random forests as well as geological insight about contrasting orogenic Au endowment in the Timmins and Dryden areas. Overall, this research demonstrates the importance of integrative studies that leverage multi- disciplinary data, methods, and knowledge to improve existing geological understanding, maximize data utility, and generate robust exploration targets. These improvements may enhance exploration under difficult conditions, such as in data sparse environments, regions affected by clustered/partial data representation, or targets under cover.